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arXiv 2022年

作者： Su, Lan Niu, Yong Han, Zhu Ai, Bo He, Ruisi Wang, Yibing Wang, Ning Su, Xiang The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Norwegian University of Science and Technology Norway and University of Oulu Finland

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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Towards Formal Specification for AADL with Behavior Annex in Isabelle

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IOP Conference Series: Earth and Environmental science 2021年第4期769卷

作者： Yu Tan Dianfu Ma Lei Qiao School of Computer Science and Engineering Beihang Univerisity Beijing 100191 China Beijing Institute of Control Engineering Beijing 100190 China

The analysis of safety-critical systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a challenging research topic. In such a context, formal methods become an advocated practice in software engineering for rigorous analysis. Moreover, they are applied on specific formalisms to be analyzed on dedicated tools. This paper studies the comprehensive formal specification for AADL language, in particular supporting major components of AADL and Behavior Annex. The presentation of this specification and modeling is the aim of this paper. This work is illustrated with a ARINC653 case study. As a study case, this work develops an AADL model from an ARINC653, specify a set of critical properties of the model and perform formal modeling in in Isabelle/HOL.

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Neural networks for disturbance and friction compensation in hard disk drives

Neural networks for disturbance and friction compensation in...

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IEEE Conference on Decision and control

作者： Chow Yin Lai Frank L. Lewis V. Venkataramanan Xuemei Ren Shuzhi Sam Ge Thomas Liew Centre for Life Sciences (CeLS) Graduate School for Integrative Sciences and Engineering (NGS) National University of Singapore Singapore Automation and Robotics Research Institute University of Texas Arlington Fort Worth TX USA Data Storage Institute Engineering Agency for Science Technology and Research Singapore Department of Automatic Control Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781424431236

In this paper, we show that the tracking performance of a hard disk drive actuator can be improved by using two adaptive neural networks, each of which is tailored for a specific task. The first neural network utilizes accelerometer signal to detect external vibrations, and compensates for its effect on hard disk drive position via feedforward action. In particular, no information on the plant, sensor and disturbance dynamics is needed in the design of this neural network disturbance compensator. The second neural network, designed to compensate for the pivot friction, uses a signum activation function to introduce nonlinearities inherent to pivot friction, thus reducing the neural network¿s burden of expectation. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Simulation results show that the tracking performance of the hard disk drives can be improved significantly with the use of both neural networks compared to the case without compensation, or when only one of the networks is activated.

关键词： Neural networks Friction Hard disks Actuators Adaptive systems Feedforward neural networks Accelerometers Signal detection Stability analysis Stability criteria

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries

Exponential Weighted Moving Average-Based Variational Mode D...

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Data Driven control and Learning Systems (DDCLS)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Lin, Jiacheng Chen, Jiajun Yang, Kailun Roitberg, Alina Li, Siyu Li, Zhiyong Li, Shutao The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China

Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce ADAPTIVECLICK – a click-aware transformer incorporating an adaptive focal loss that tackles annotation inconsistencies with tools for mask- and pixel-level ambiguity resolution. To the best of our knowledge, AdaptiveClick is the first transformer-based, mask-adaptive segmentation framework for IIS. The key ingredient of our method is the Click-Aware Mask-adaptive transformer Decoder (CAMD), which enhances the interaction between click and image features. Additionally, AdaptiveClick enables pixel-adaptive differentiation of hard and easy samples in the decision space, independent of their varying distributions. This is primarily achieved by optimizing a generalized Adaptive Focal Loss (AFL) with a theoretical guarantee, where two adaptive coefficients control the ratio of gradient values for hard and easy pixels. Our analysis reveals that the commonly used Focal and BCE losses can be considered special cases of the proposed AFL. With a plain ViT backbone, extensive experimental results on nine datasets demonstrate the superiority of AdaptiveClick compared to state-of-the-art methods. The source code is publicly available at https://***/lab206/AdaptiveClick. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Active Collision Avoidance Maneuver Design Under Constant Thrust ⋆

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IFAC Proceedings Volumes 2011年第1期44卷 9887-9892页

作者： Yongqiang Qi Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

Abstract In this paper, the relative position parameters of the target spacecraft are obtained by using the vision measurement and the target maneuver positions are calculated through the isochronous interpolation method. Furthermore, new switch control laws under constant thrust are designed for active collision avoidance maneuver of the chaser along a specified trajectory. The switch control laws are obtained based on the acceleration sequences and the working times of thrusters in three axes which can be respectively computed by the time series analysis method. The perturbations and fuel consumptions are addressed during the computation of the working times. The simulation results show that the constant thrust maneuver can effectively guarantee the chaser maneuvering along the specified trajectory.

关键词： Active collision avoidance maneuver Collision possibility Constant thrust Switch control laws

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Practical implementation of a model inverse optimal iterative learning controller

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IFAC Proceedings Volumes 2004年第12期37卷 687-692页

作者： J.D. Ratcliffe T.J. Harte J.J. Hätönen P.L. Lewin E. Rogers D.H. Owens School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ United Kingdom Fax: +44 (0)23 8059 3709 Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom Systems Engineering Laboratory The University of Oulu P.O. BOX 4300 FIN-90014 University of Oulu Finland

In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence rate the lack of robustness is a major issue. Appropriate use of the tuning parameter is shown to greatly increase the algorithm robustness as demonstrated by tests which successfully reach 600 iterations.

关键词： Learning control Iterative methods Optimal control

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Enhanced valley zeeman splitting in fe-doped monolayer mos2

arXiv

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arXiv 2020年

作者： Li, Qi Zhao, Xiaoxu Deng, Longjiang Shi, Zhongtai Liu, Sheng Wei, Qilin Zhang, Linbo Cheng, Yingchun Zhang, Li Lu, Haipeng Gao, Weibo Huang, Wei Qiu, Cheng-Wei Xiang, Gang Pennycook, Stephen John Xiong, Qihua Loh, Kian Ping Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Chemistry Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore 117549 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Nanjing Tech University Nanjing211816 China College of Physical Science and Technology Sichuan University Chengdu610064 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The "Zeeman effect" offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (geff = -6.4) and 10 K (geff = -11) in a CVD-grown Fe-doped MoS2 monolayer;the effective Landé geff factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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Improved Multidisciplinary Multiobjective Collaborative Optimization Technique Based on NSGA-II Algorithm 1

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IFAC Proceedings Volumes 2010年第25期43卷 139-144页

作者： H. Li Y. Jing S. Zhang V. Ojleska T. Kolemisevska G.M. Dimirovski Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110004 P.R. China Northeastern University School of Information Science & Engineering Shenyang 110004 P.R. China SS Cyril and Methodius University School FEIT Institute of ASE Karpos II BB MK-1000 Skopje R. Macedonia Dogus University Computer Eng. and Control Eng. Departments TR-34722 Istanbul Turkey

The multiobjective collaborative optimization (MCO) has been widely adopted in concurrent engineering design as a good multiobjective optimization approach provided the problem of optimization results converging often to a local extremum has been taken care of. It has been demonstrated to yield useful solutions to a wide range of optimization problems under concurrent condition both technical and non-technical ones. A solution strategy for MCO that overcomes this drawback is proposed here on the grounds of genetic NSGA-II algorithm. It is an adaptive heuristic search technique that controls the distances between the individual and system's feasible region during genetic evolution. In the evolving NSGA-II based non-dominated sorting, the individual threshold of infeasibility degree is reduced gradually via genetic evolution and the individual's feasibility capacity is determined by its infeasibility degree and the threshold. It has been applied to a benchmark example of speed reducer design, and the obtained results demonstrate the proposed technique is comparably more efficient and may offer substantial advantages over the gradient-based optimization methods. Copyright © 2010 IFAC

关键词： Collaborative optimization dynamic threshold genetic algorithms multidisciplinary optimization multiobjective optimization

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression

A study on the prediction model of dam seepage volume based ...

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International Conference on computer engineering and Applications (ICCEA)

作者： Guanyu Chen Suli Zhang Chenlin Zhang XiaoChun Qian Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun University of Technology School of Computer Science & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun Institute of Technology School of Computer Technology & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Jilin Institute of Chemical Technology School of Information and Control Engineering Changchun China

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features.

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